AI+ Engineer™ eLearning

Innovate Engineering: Leverage AI-Driven Smart Solutions

• Full AI Stack: Learn AI architecture, LLMs, NLP, and neural networks

• Tool Proficiency: Includes Transfer Learning with Hugging Face and GUI design

• Deployment Focus: Build real AI systems and manage communication pipelines

• Practical Mastery: Gain the skills to engineer scalable AI solutions for innovation

Course Overview

• Course Introduction Preview

Module 1: Foundations of Artificial Intelligence

1. 1.1 Introduction to AI Preview
2. 1.2 Core Concepts and Techniques in AI Preview
3. 1.3 Ethical Considerations Module 2: Introduction to AI Architecture
   1. 2.1 Overview of AI and its Various ApplicationsPreview
   2. 2.2 Introduction to AI Architecture Preview
   3. 2.3 Understanding the AI Development Lifecycle Preview
   4. 2.4 Hands-on: Setting up a Basic AI Environment Module 3: Fundamentals of Neural Networks
      1. 3.1 Basics of Neural Networks Preview
      2. 3.2 Activation Functions and Their Role Preview
      3. 3.3 Backpropagation and Optimization Algorithms
      4. 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework Module 4: Applications of Neural Networks
         • 4.1 Introduction to Neural Networks in Image Processing
         • 4.2 Neural Networks for Sequential Data
         • 4.3 Practical Implementation of Neural Networks
         Module 5: Significance of Large Language Models (LLM)
         • 5.1 Exploring Large Language Models
         • 5.2 Popular Large Language Models
         • 5.3 Practical Finetuning of Language Models
         • 5.4 Hands-on: Practical Finetuning for Text Classification
         Module 6: Application of Generative AI
         • 6.1 Introduction to Generative Adversarial Networks (GANs)
         • 6.2 Applications of Variational Autoencoders (VAEs)
         • 6.3 Generating Realistic Data Using Generative Models
         • 6.4 Hands-on: Implementing Generative Models for Image Synthesis
         Module 7: Natural Language Processing
         • 7.1 NLP in Real-world Scenarios
         • 7.2 Attention Mechanisms and Practical Use of Transformers
         • 7.3 In-depth Understanding of BERT for Practical NLP Tasks
         • 7.4 Hands-on: Building Practical NLP Pipelines with Pretrained Models
         Module 8: Transfer Learning with Hugging Face
         • 8.1 Overview of Transfer Learning in AI
         • 8.2 Transfer Learning Strategies and Techniques
         • 8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks
         Module 9: Crafting Sophisticated GUIs for AI Solutions
         • 9.1 Overview of GUI-based AI Applications
         • 9.2 Web-based Framework
         • 9.3 Desktop Application Framework
         Module 10: AI Communication and Deployment Pipeline
         • 10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
         • 10.2 Building a Deployment Pipeline for AI Models
         • 10.3 Developing Prototypes Based on Client Requirements
         • 10.4 Hands-on: Deployment
         Optional Module: AI Agents for Engineering
         • 1. Understanding AI Agents
         • 2. Case Studies
         • 3. Hands-On Practice with AI Agents
         Tools you will explore
         • TensorFlow
         • Hugging Face Transformers
         • Jenkins
         • TensorFlow Hub

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

Instructor-led OR Self-paced course + Official exam + Digital badge